Abstract

At present, the application of machine vision methods for roughness measurement in production sites is limited by its adaptability to illumination variations during the measurement. In this study, a machine vision method for roughness measurement with robustness to illumination is proposed so as to explore the functions of its color image indices in improving the mathematical expression of the vector of three primary colors. Besides, virtual images of different-roughness surfaces were analyzed, the effects of the samples’ surface texture orientations on measurement indices were discussed, and the singular value ratio was derived as an index for evaluating roughness. The experimental results showed that the samples’ index values remained unchanged when the illumination was increased for both vertical and horizontal surface textures, indicating that the proposed method has strong robustness to illumination. In addition, the experimental results were verified by a support vector machine (SVM)-based method using 10 different-roughness test samples, with the verification range of 0.127–2.245 µm. It was found that the measurement accuracy reached 90%, suggesting that the proposed method is reasonable and feasible, and shows certain potential to be applied in engineering.

© 2021 Optical Society of America

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